基于向量式有限元的黏滞阻尼减震结构抗竖向连续倒塌动力反应分析          下载免费PDF全文

曲激婷  宋全宝 《地震工程学报》2019,41(6):1432-1439

向量式有限元是以向量力学为基础的一种新的结构分析方法,在处理结构大变形等复杂行为时具有较大的优势。基于向量式有限元理论建立了黏滞阻尼单元,对附加黏滞阻尼器的平面钢框架结构进行了抗竖向连续倒塌动力分析,结合拆除构件法,采用MATLAB编制可以考虑初始变形的瞬时卸载法程序,实现结构在构件拆除前的静力分析和构件拆除后动力分析的全过程统一。研究了阻尼器布置位置和参数在结构抗竖向连续倒塌中的性能需求,以失效点竖向位移时程曲线、梁端转角、动力放大系数和结构塑性铰分布为参考指标,对比分析布置阻尼器前后钢框架结构的抗连续倒塌能力。结果表明向量式有限元是一种研究结构竖向连续倒塌动力响应的有效方法,合理布置阻尼器能够有效控制剩余结构的变形和振动,降低构件内力,减少塑性铰个数,较大地提高结构的抗竖向连续倒塌能力。  相似文献   

基于自相似河网结构的河网消退系数Cs计算方法研究     

臧帅宏  李致家  黄迎春  李巧玲 《湖泊科学》2019,31(3):788-800

新安江模型河网汇流参数Cs对洪峰模拟影响较大，目前Cs的确定需依赖于大量的历史数据，因此Cs的确定成为无资料地区和资料匮乏区水文模型应用中亟需解决的棘手问题.本文基于参数的物理意义，通过自相似河网结构的假定，构建Cs与河网形态、流域下垫面特征的相关联系，提出基于河链蓄量方程的Cs估算方法，对半干旱、半湿润和湿润地区等不同水文气象分区的11个流域的Cs值进行推算并代入新安江模型中进行模拟，经比较发现，11个流域子流域Cs计算均值与新安江模型率定结果相近，说明该Cs计算方法是合理的.选取陈河、屯溪两个典型流域研究单元流域属性对Cs的影响，由结果可以看出Cs与流域面积、河链数、河宽呈正相关，与单元流域距离出口的远近呈负相关，这表明流域分块后各单元流域Cs值不一致，而新安江模型中采用相同Cs值对不同单元进行调节必然会造成汇流计算的误差.为进一步提高该方法在无资料地区的应用效果，将新安江模型汇流模块修改为每个单元使用对应的Cs计算值进行滞后演算，以陈河和屯溪流域为例采用新安江模型Cs率定值、Cs计算均值以及修改后新安江模型3种不同方案进行模拟比较，从模拟结果可以得出，修改后的模型具有明显优势，将模型参数与下垫面条件建立了联系，模型物理机制提高且参数的独立性增强，对于新安江模型在无资料地区的应用具有重要的指导意义.  相似文献   

Modeling RC column flexural failure modes under intensive seismic loading     

Ilias Gkimousis  Vlasis Koumousis 《地震工程与结构动力学》2018,47(9):1942-1962

The aim of this work is to model beam‐column behavior in a computationally effective manner, revealing reliably the overall response of reinforced concrete members subjected to intensive seismic loading. In this respect, plasticity and damage are considered in the predominant longitudinal direction, allowing for fiber finite element modeling, while in addition the effect of inelastic buckling of longitudinal rebars, which becomes essential at later stages of intensive cyclic loading, is incorporated. Α smooth plasticity‐damage model is developed for concrete, accounting for unilateral compressive and tensile behavior, nonlinear unloading and crack closure phenomena. This is used to address concrete core crushing and spalling, which triggers the inelastic buckling of longitudinal rebars. For this reason, a uniaxial local stress‐strain constitutive relation for steel rebars is developed, which is based on a combined nonlinear kinematic and isotropic hardening law. The proposed constitutive model is validated on the basis of existing experimental data and the formulation of the buckling model for a single rebar is developed. The cross section of rebar is discretized into fibers, each one following the derived stress‐strain uniaxial law. The buckling curve is determined analytically, while equilibrium is imposed at the deformed configuration. The proposed models for concrete and rebars are embedded into a properly adjusted fiber beam‐column element of reinforced concrete members and the proposed formulation is verified with existing experimental data under intensive cyclic loading.  相似文献   

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?     

《地学前缘(英文版)》2020,11(3):871-883

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.  相似文献   

基于GIS的白龙江流域舟曲—武都段的滑坡危险性评价   总被引：1，自引：0，他引：1       下载免费PDF全文

刘杰  武震 《地震工程学报》2020,42(6):1723-1734

本研究以围绕着白龙江流域的甘肃省南部的宕昌县、舟曲县和武都区部分地区为研究区，根据全国滑坡编目中得到的272个历史滑坡数据以及选取的高程、坡度、坡向、平面曲率、剖面曲率、归一化植被指数（NDVI）、降雨、岩性、距道路距离和距河流距离10种影响因子，利用三种具有代表性的定量方法：信息量模型、以及基于频率比模型的逻辑回归模型和人工神经网络模型对研究区内滑坡灾害危险性进行评价。三种评价结果均显示研究区内滑坡灾害的极高和高危险区主要沿白龙江河谷地区呈带状分布。从危险性分区图可看出，人工神经网络模型得到的分区图较为合理，既表现出沿河谷地区集中分布的趋势，也呈现出对滑坡历史数据较为独立的特征，这一研究结果与前人研究结果一致。根据受试者工作特征曲线（ROC曲线）对三种模型的精度进行检验，检验得到的AUC值分别为0.818、0.829和0.837，说明三种评价结果均具有较高的可靠性，基于频率比模型的人工神经网络模型相比其他两个模型具有更好的评价精度，能更好地进行滑坡危险性的预测和评价，其中高程、降雨、岩性以及距道路距离对评价结果影响更大，这四种影响因子重要性值占比为52.1%。为该地区的城市扩建与灾害预防预测提供了参考。  相似文献   

Estimating the starting time and identifying the type of urbanization based on dense time series of landsat-derived Vegetation-Impervious-Soil (V-I-S) maps – A case study of North Taiwan from 1990 to 2015     

《International Journal of Applied Earth Observation and Geoinformation》2020

Land cover and land use change (LCLUC) is a global phenomenon, and LCLUC in urbanizing regions has substantial impacts on humans and their environments. In this paper, a semi-automatic approach to identifying the type and starting time of urbanization was developed and tested based on dense time series of Vegetation-Impervious-Soil (V-I-S) maps derived from Landsat surface reflectance imagery. The accuracy of modeled V-I-S fractions and the estimated time of initial change in impervious cover were assessed. North Taiwan, one of the regions of the island of Taiwan that experienced the greatest urban LCLUC, was chosen as a test area, and the study period is 1990 to 2015, a period of substantial urbanization. In total, 295 dates of Landsat imagery were used to create 295 V-I-S fraction maps that were used to construct fractional cover time series for each pixel. Root Mean Square Error (RMSE)s for the modeled Vegetation, Impervious, and Soil were 25 %, 22 %, 24 % respectively. The time of Urban Expansion is estimated by logistic regression applied to Impervious cover time series, while the time of change for Urban Renewal is determined by the period of brief Soil exposure. The identified location and estimated time for newly urbanized lands were generally accurate, with 80% of Urban Expansion estimated within ±2.4 years. However, the accuracy of identified Urban Renewal was relatively low. Our approach to identifying Urban Expansion with dense time series of Landsat imagery is shown to be reliable, while Urban Renewal identification is not.  相似文献   

利用多元回归分析反演西南印度洋区域海底地形     

范雕  李姗姗  杨军军  孟书宇  邢志斌  张驰  冯进凯 《测绘学报》2020,49(2):147-161

针对海底地形与重力异常和重力异常垂直梯度在相应频段呈现强线性相关的特点,引入多元回归分析技术,提出并详细推导了联合多元重力数据的海底地形建模方法。然后,在西南印度洋SWIR(Southwest India Ridge)所在部分海域开展了海底地形反演试验及地形地貌分析研究。试验结果表明:6种海深模型中,基于多元回归分析技术构建的海深模型(BDVG模型)检核精度最高,相较于S&S V18.1模型和ETOPO1模型精度分别提高了11.51%和57.81%左右;2000 m以上水深海域,各个海深模型的检核精度较高,相对误差波动较小,反映了深海海域具有良好的反演效果;地形起伏剧烈海域或者浅海海域,BDVG海深模型,相较于以重力异常和重力异常垂直梯度作为单一输入源建立的BDG模型和BVGG模型相对误差及相对误差波动变化较小,反映了BDVG模型拥有更好的稳定性,从而体现了联合反演的必要性和优势。Indomed FZ—Gallieni FZ上唯一轴部缺失裂谷洋脊段(27洋脊段)目前属于岩浆供应充足阶段,构造作用的海底扩张对其影响较小;同时由于对称裂离方式影响,27洋脊段沿轴南北对称分布有地形隆起。  相似文献   

基于广义回归神经网络的行星际/太阳风参数和地磁指数的紫外极光强度建模          下载免费PDF全文

胡泽骏  韩冰  连慧芳 《地球物理学报》2020,63(5):1738-1750

极光卵极光强度的空间分布是太阳风-磁层-电离层能量耦合过程的重要表现,并且随着空间环境参数和地磁指数的变化而变化,是空间天气的重要指示器.建立合适的极光强度模型对亚暴的预测以及磁层动力学的研究具有重要意义.本文基于Polar卫星的紫外极光成像仪(Ultraviolet Imager,UVI)数据,采用两种不同的极光强度表征方法,即曲线拟合方法(从UVI图像数据中提取极光强度沿磁余纬方向上的曲线特征,Curve Feature along the Magnetic Co-latitude Direction of the Auroral Intensity,CFMCD_AI)和网格化方法(从UVI图像数据中提取极光强度的网格化特征,Gridding Feature of the Auroral Intensity,GF_AI),来构造极区极光强度特征数据库.然后,利用该数据库,采用广义回归神经网络(Generalized Regression Neural Network,GRNN)构建了以行星际/太阳风参数(行星际磁场三分量、太阳风速度和密度)和地磁指数(AE指数)为输入参数的两种极光强度预测模型(GRNN_CFMCD_AI模型和GRNN_GF_AI模型).利用图像质量评价指数结构相似度(structure similarity,SSIM)作为极光强度模型预测结果和对应的UVI图像的相似性评价标准(完全相似为1,不相似为0,一般认为SSIM大于0.5是具有较好的相似性),对两种极光强度模型进行了性能评价.结果显示,GRNN_GF_AI模型预测结果对应的SSIM值范围为0.36～0.77,均值为0.54,性能优于GRNN_CFMCD_AI模型的.  相似文献   

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity     

《International Journal of Applied Earth Observation and Geoinformation》2020

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.  相似文献   

融合多源时空数据的城市火灾危险性评估     

赵晓旭 《测绘通报》2020,(5):101-106

城市层面的火灾风险评估主要包括火灾危险性、危害性及救援能力等方面。本文选取火灾危险性评估进行针对性研究，在大数据思维的指导下，以相关关系代替因果关系，采用多源数据对评估指标权重、分值进行率定，得出福州市城区火灾危险性时空分布图。首先利用高德地图API对消防历史出警记录进行地址解析，将近万条火灾出警地址空间落点，获得福州市历史火灾空间分布；然后综合城市用地性质现状、用地开发性质、人口分布热力图等多源异构数据，探索其与历史火灾空间分布的相关性；最后以福州城区为例，初步实现具有充分数理支撑的火灾危险性评估方法，形成火灾危险性动态评估成果，为城市消防规划等提供支撑和依据。  相似文献